Proposal of neural network model for neurocognitive rehabilitation and its comparison with fuzzy expert system model.
ACE-R
CHC
Fuzzy expert system model
Neural network model
Neurocognitive rehabilitation
Journal
BMC medical informatics and decision making
ISSN: 1472-6947
Titre abrégé: BMC Med Inform Decis Mak
Pays: England
ID NLM: 101088682
Informations de publication
Date de publication:
16 10 2023
16 10 2023
Historique:
received:
22
05
2023
accepted:
02
10
2023
medline:
23
10
2023
pubmed:
17
10
2023
entrez:
16
10
2023
Statut:
epublish
Résumé
This article focuses on the development of algorithms for a smart neurorehabilitation system, whose core is made up of artificial neural networks. The authors of the article have proposed a completely unique transfer of ACE-R results to the CHC model. This unique approach allows for the saturation of the CHC model domains according to modified ACE-R factor analysis. The outputs of the proposed algorithm thus enable the automatic creation of a personalized and optimized neurorehabilitation plan for individual patients to train their cognitive functions. A set of tasks in 6 levels of difficulty (level 1 to level 6) was designed for each of the nine CHC model domains. For each patient, the results of the ACE-R screening helped deter-mine the specific CHC domains to be rehabilitated, as well as the initial gaming level for rehabilitation in each domain. The proposed artificial neural network algorithm was adapted to real data from 703 patients. Experimental outputs were compared to the outputs of the initially designed fuzzy expert system, which was trained on the same real data, and all outputs from both systems were statistically evaluated against expert conclusions that were available. It is evident from the conducted experimental study that the smart neurorehabilitation system using artificial neural networks achieved significantly better results than the neurorehabilitation system whose core is a fuzzy expert system. Both algorithms are implemented into a comprehensive neurorehabilitation portal (Eddie), which was supported by a research project from the Technology Agency of the Czech Republic.
Identifiants
pubmed: 37845677
doi: 10.1186/s12911-023-02321-1
pii: 10.1186/s12911-023-02321-1
pmc: PMC10580608
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
221Informations de copyright
© 2023. BioMed Central Ltd., part of Springer Nature.
Références
Neurology. 2000 Dec 12;55(11):1613-20
pubmed: 11113213
JMIR Mhealth Uhealth. 2020 Jun 24;8(6):e15517
pubmed: 32442150
J Rehabil Med. 2020 Sep 8;52(9):jrm00099
pubmed: 32896864
Brain Sci. 2022 Dec 07;12(12):
pubmed: 36552138
J Stroke Cerebrovasc Dis. 2015 Dec;24(12):2660-8
pubmed: 26483155
Sensors (Basel). 2020 May 09;20(9):
pubmed: 32397516
IEEE J Biomed Health Inform. 2019 May;23(3):1269-1277
pubmed: 30668485
Brain Inj. 2020 Aug 23;34(10):1322-1330
pubmed: 32791020
Biomed Res Int. 2021 Jun 18;2021:9967348
pubmed: 34239936
IEEE J Biomed Health Inform. 2022 Dec;26(12):6003-6011
pubmed: 36083954
IEEE J Biomed Health Inform. 2015 Jan;19(1):124-31
pubmed: 25204001
Neural Plast. 2018 May 6;2018:2651918
pubmed: 29853840
Expert Rev Neurother. 2019 Jun;19(6):471-473
pubmed: 31090484
Front Aging Neurosci. 2016 Dec 20;8:313
pubmed: 28066236
Alzheimers Dement (N Y). 2019 Nov 22;5:834-850
pubmed: 31799368
Arch Clin Neuropsychol. 2020 Feb 20;35(2):205-212
pubmed: 31875877
Behav Neurol. 2019 Nov 5;2019:8241951
pubmed: 31781294
JMIR Aging. 2019 Feb 27;2(1):e13135
pubmed: 31518277
Cochrane Database Syst Rev. 2017 Nov 20;11:CD008349
pubmed: 29156493